Abstract
Botnets widely use DGA (Domain Generation Algorithm) technology to evade network security detection, and DGA malicious domain name detection has attracted much attention. Aiming at the problem that poor feature extraction effect and low detection accuracy of existing domain name detection methods, this paper proposes a hybrid neural network model based on CNN-LSTM. The model first uses multi-channel Convolutional Neural Network (CNN) to extract the NGram features of domain names; then uses Long Short-Term Memory (LSTM) to extract the contextual grammar features of domain names; finally introduces the attention mechanism to assign different weights for the extracted domain name features, focusing on more critical information. The experiment results illustrate the proposed model maintains an Accuracy of 99.02% in malicious domain name detection, which can obtain higher detection accuracy than the existing domain name detection model.
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